The relationship between delay discounting and Internet addiction: A systematic review and meta-analysis
Introduction
Among different behavioral measures of impulse control, delay discounting (DD) task is often used for evaluating the capacity to tolerate delay in reward (Dougherty, Mathias, Marsh, & Jagar, 2005). The DD tasks consist of a series of choices between immediately available smaller rewards and greater rewards available only after some length of time (Matta, Gonçalves, & Bizarro, 2012). Preferring immediate rewards to potentially more satisfying experience later is considered to indicate a poor impulse control (Hoffman et al., 2008, Monterosso et al., 2007). In addition to the DD task, existing literature also employs the delayed gratification (DG) task to assess one’s ability to withstand a delay in reward; however, Reynolds and Schiffbauer (2005) demonstrated that the processes involved in the DD task required a higher level of cognitive function and more learning-mediated ability than those required by the DG task. Thus, the DG task is more suitable for young children, while the DD task is used preferably for adolescents and adults (Göllner, Ballhausen, Kliegel, & Forstmeier, 2018). Actually, DD is more often used to measure impulse control in different preclinical and clinical groups (Amlung et al., 2019, Matta et al., 2012), such as subjects with attention-deficit/hyperactivity disorder (ADHD) (Wilson, Mitchell, Musser, Schmitt, & Nigg, 2011), schizophrenia (Brown et al., 2018, Heerey et al., 2007), bipolar disorder (Urošević, Youngstrom, Collins, Jensen, & Luciana, 2016), obesity (Appelhans et al., 2011, Weller et al., 2008) as well as gambling (Dixon, Marley, & Jacobs, 2003) and addiction (Robles et al., 2011, Saville et al., 2010). Furthermore, several meta-analyses were conducted to give reliable proof of a steeper discounting in individuals with ADHD (Weicker, Villringer, & Thöne-Otto, 2016), obesity (Amlung, Petker, Jackson, Balodis, & MacKillop, 2016), gambling (MacKillop et al., 2014) and addiction (Amlung et al., 2017, MacKillop et al., 2011) compared to those without. Recently, Amlung et al. (2019) preformed a meta-analysis and showed that DD could be used as a transdiagnostic criterion across many mental disorders.
Although DD can be assessed by using a variety of techniques (Madden & Johnson, 2010), they are conducted on a hypothetical and experiential basis. Indeed, hypothetical rewards and delays are involved in the majority of human DD research (Odum, 2011). Moreover, even using real reward, a previous investigation did not detect a significant correlation between reward type (real and hypothetical money) and DD (Madden, Begotka, Raiff, & Kastern, 2003). Subjects indifferently chose two alternatives (small but immediate reward vs. larger but delayed reward), namely the indifference points, across a series of intertemporal choices, expressed as DD function. Two mathematical models are used to explain the discounting curve (Myerson & Green, 1995), including the exponential (Lancaster, 1963, Meyer, 1976) and hyperbolic equations (de Villiers and Herrnstein, 1976, Mazur, 1987). The hyperbolic equation can provide a good fit for individuals’ preferences according to existing research evidence (Madden et al., 2003). Mazur (1987) was the first to adopt a rate parameter from a hyperbolic function to quantify DD data:where Vd is the subjective value of delayed gain, D is the delay, and k is the discounting parameter (Koffarnus et al., 2017, Mazur, 1987). The higher the k value, the steeper the decrease in the subjective value of delayed gain (see Fig. 1). The reverse is also true. Therefore, k value is regarded as the impulsivity index. It is the specific DD rate for an individual participant determined by the transition of this curve from asymptotic values near 1.0 at lower delays to asymptotic values near 0.0 at higher delays. In addition to k value, the area under the curve (AUC) is used to analyzed DD. Myerson, Green, and Warusawitharana (2001) suggested the use of AUC, instead of k value, to measure DD for two main reasons: (1) the distribution of area measures, unlike distributions of estimates of the parameters, is not skewed; (2) unlike measures based on the parameters of a discounting function, the area measure requires no assumptions regarding the mathematical form of this function (Myerson et al., 2001). In contrast to k value, the AUC score as impulsivity reverses from 0 (no delay) to 1 (maximum delay) (Myerson et al., 2001). In other words, larger AUCs represent stronger inhibition for immediate reward (i.e., less discounting by delay), that is, subject are less impulsive (or more self-controlled) (Odum, 2011).
Regarding addiction, DD is further regarded as an endophenotype (MacKillop, 2013) that is potentially useful to identify the influenced attribute of a disorder-predisposing genotype (Gottesman & Gould, 2003). Although two previous meta-analyses have shown that individuals with substance addiction had poorer performance on the DD task (i.e., steeper discounting of delayed rewards) (Amlung et al., 2017, MacKillop et al., 2011), findings from subjects with substance addiction may not be extrapolated to those with IA as evidence suggests that IA, a behavioral addiction, involves a long period of fluctuating cognitive control (LaRose et al., 2003, Polivy, 1998) rather than neuropharmacological mechanisms per se (Kauer & Malenka, 2007). In other words, IA is more closely associated with the cognitive process that allows the individual to compare values between the immediate and delayed rewards (i.e., DD task) (Loewenstein, 1988, Matta et al., 2012). However, the relationship between DD and IA has not been systematically reviewed and analyzed.
At least five types of IA are identified, such as Internet gaming addiction (Kuss & Griffiths, 2012), cybersex (Delmonico, 1997), online shopping addiction (Rose & Dhandayudham, 2014), social networking (media) addiction (Griffiths, Kuss, & Demetrovics, 2014), and Internet gambling addiction (Griffiths & Parke, 2008). The previous study by Kuss & Griffiths (2012) supported the idea that Internet gaming addiction can be regarded as an addictive disorder rather than an impulse-control disorder. Following a provisional criterion for Internet gaming disorder (IGD) in the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (Petry and O'Brien, 2013, Petry et al., 2015), gaming disorder was officially adopted at the World Health Assembly in May 2019 as a diagnosis in the eleventh edition of the International Classification of Diseases (ICD-11; World Health Organization, 2019). A distinctive diagnosis of IGD suggests that it may differ from the other types of IA (Griffiths et al., 2016, Pontes and Griffiths, 2014). Existing studies also found that subjects with different types of IA showed their respective characteristics, such as gender and psychological factors (Collins et al., 2012, Hong et al., 2012, Kircaburun et al., 2020, Park et al., 2007, Ryan et al., 2014, Wang et al., 2015). The debate about addictions on the Internet versus addictions to the Internet is still ongoing (Billieux, 2012, Griffiths et al., 2016). DD is used to estimate the impulsivity across the types of IA, such as Internet gaming (Tian et al., 2018), gambling (Stieg & Dixon, 2007), online shopping (Hantula, Brockman, & Smith, 2008), and cybersex (Negash, Sheppard, Lambert, & Fincham, 2016). However, whether subjects with different types of IA have similar DD may need to be clarified.
The primary purpose of this meta-analysis was to investigate the difference in DD between subjects with IA and those without. Additionally, we looked for the significant variables which influenced DD (e.g., data analysis methods, modes of administration, and gender).
Section snippets
Study eligibility and definitions
Addictive behavior is regarded as compulsive use, dependence, overuse, and abuse. Besides “problematic” or “pathological” Internet use (Griffiths et al., 2016), a wide range of nomenclature across the psychological, psychiatric, and neuroscientific literature has been used to refer to IA (Pontes, Kuss, and Griffiths (2015). Therefore, different terms were used to ensure the completeness of literature search for the current study, namely, Internet addiction OR problematic Internet use OR
Study characteristics
The data from 14 studies were included in the current meta-analysis. Participants included 696 subjects of IA (mean age = 22.71 years) and 2,394 healthy subjects (mean age = 21.91 years). The characteristics of the selected studies are listed in Table 1. Subjects’ age was over 18 years in most studies except the study by Tian et al. (2018).There was a male predominance as expected. Young’s diagnostic questionnaire for Internet addiction (YDQ) and Young’s Internet addiction test (YIAT) were the
Main findings
The present study had several notable findings. First, the DD rate in the IA group was steeper than that in its comparators, indicating that subjects with IA had greater impulsivity. Second, because of the significant difference in results between k value and AUC function in discounting rate analysis, judicious use of the two methods is suggested. Third, the studies conducted by the paper-and-pencil task showed a steeper DD rate compared to that in those using computerized administration.
Conclusion
The current study demonstrated a significant difference in DD between subjects with IA and those without. Judicious use of analysis methods for representing DD is suggested because of a significant difference in results when different methods were used. The modes of administration may actually influence the performance of DD. The comparisons between subjects with different types of IA in the DD rate were worth further exploration. The significant correlation between the DD rate and task
CRediT authorship contribution statement
Yu-Shian Cheng: Data curation, Investigation, Project administration, Writing - original draft. Huei-Chen Ko: Supervision, Writing - review & editing. Cheuk-Kwan Sun: Writing - review & editing. Pin-Yang Yeh: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Writing - original draft.
References (105)
- et al.
Intention, Action Planning, and Decision Making in Parietal-Frontal Circuits
Neuron
(2009) Academic delay of gratification and expectancy–value
Personality and Individual Differences
(2008)- et al.
Remember the future: Working memory training decreases delay discounting among stimulant addicts
Biological Psychiatry
(2011) - et al.
Personality traits associated with problematic and non-problematic massively multiplayer online role playing game use
Personality and Individual Differences
(2012) - et al.
Impulsivity, compulsivity, and top-down cognitive control
Neuron
(2011) - et al.
IQ and nonplanning impulsivity are independently associated with delay discounting in middle-aged adults
Personality and Individual Differences
(2007) - et al.
Social networking addiction: An overview of preliminary findings
- et al.
A model of the relationship between psychological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students
Computers in Human Behavior
(2012) - et al.
An adaptive, individualized fMRI delay discounting procedure to increase flexibility and optimize scanner time
NeuroImage
(2017) - et al.
Internet addiction in adolescents: Prevalence and risk factors
Computers in Human Behavior
(2013)
Does goal relevant episodic future thinking amplify the effect on delay discounting?
Consciousness and Cognition
Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions
Neuron
Measuring state changes in human delay discounting: An experiential discounting task
Behavioural Processes
Delay discounting, impulsiveness, and addiction severity in opioid-dependent patients
Journal of Substance Abuse Treatment
Fifty years of the Barratt Impulsiveness Scale: An update and review
Personality and Individual Differences
Recognizing internet addiction: Prevalence and relationship to academic achievement in adolescents enrolled in urban and rural Greek high schools
Journal of Adolescence
Internet gaming disorder in adolescents is linked to delay discounting but not probability discounting
Computers in Human Behavior
Associations of age with reward delay discounting and response inhibition in adolescents with bipolar disorders
Journal of Affective Disorders
Relationship between Internet addiction and academic performance among foreign undergraduate students
Procedia-Social and Behavioral Sciences
Measuring facets of reward sensitivity, inhibition, and impulse control in individuals with problematic Internet use
Psychiatry Research
Exploring personality characteristics of Chinese adolescents with internet-related addictive behaviors: Trait differences for gaming addiction and social networking addiction
Addictive Behaviors
Obese women show greater delay discounting than healthy-weight women
Appetite
Remember the Future II: Meta-analyses and Functional Overlap of Working Memory and Delay Discounting
Biological Psychiatry
Discounting delayed monetary rewards and decision making in behavioral addictions – A comparison between patients with gambling disorder and internet gaming disorder
Addictive Behaviors
Relationship between internet addiction and academic performance among university undergraduates
Educational Research and Reviews
Delay Discounting as a Transdiagnostic Process in Psychiatric Disorders: A Meta-analysis
JAMA Psychiatry
Steep discounting of delayed monetary and food rewards in obesity: A meta-analysis
Psychological Medicine
Steep delay discounting and addictive behavior: A meta-analysis of continuous associations
Addiction
Inhibiting food reward: Delay discounting, food reward sensitivity, and palatable food intake in overweight and obese women
Obesity
Problematic use of the mobile phone: A literature review and a pathways model
Current Psychiatry Reviews
Introduction to meta-analysis
A basic introduction to fixed-effect and random-effects models for meta-analysis
Research Synthesis Methods
An alternative approach to calculating Area-Under-the-Curve (AUC) in delay discounting research
Journal of the Experimental Analysis of Behavior
Episodic future thinking is related to impulsive decision making in healthy adolescents
Child Development
Impairment in delay discounting in schizophrenia and schizoaffective disorder but not primary mood disorders
NPJ Schizophrenia
Goal-direction and top-down control
Philosophical Transactions of the Royal Society B: Biological Sciences
Intertemporal choice
University students' Internet use and its relationships with academic performance, interpersonal relationships, psychosocial adjustment, and self-evaluation
CyberPsychology & Behavior
Mode of administration bias
Journal of Manual & Manipulative Therapy
The future is now: Reducing impulsivity and energy intake using episodic future thinking
Psychological Science
Toward a law of response strength
Psychological Bulletin
Cybersex: High tech sex addiction
Sexual Addiction & Compulsivity: The Journal of Treatment and Prevention
Delay discounting by pathological gamblers
Journal of Applied Behavior Analysis
Laboratory behavioral measures of impulsivity
Behavior Research Methods
Trim and fill: A simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis
Biometrics
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2023, Journal of Psychiatric ResearchCritical appraisal of the discussion on delay discounting by Bailey et al. and Stein et al.: A scientific proposal for a reinforcer pathology theory 3.0
2023, New Ideas in PsychologyCitation Excerpt :The first critique relates to the claim that the DD rate measures impulsivity and self-control, variables associated with several psychological problems, including addiction (Amlung et al., 2019). Bailey et al. (2021) highlighted the modest (despite their significance) correlations of DD with addiction severity (e.g., Amlung & MacKillop, 2014; Gowin et al., 2019; Martinez-Loredo et al., 2018a; Strickland et al., 2021) and the evidence of no, low or inconsistent associations with other relevant psycho(patho)logical phenomena (Cheng et al., 2021; Farris et al., 2017; Martinez-Loredo et al., 2019). Against this background, the question about how ‘core’ could DD rates be for psychopathology is of great importance.
Delay discounting in Parkinson's disease: A systematic review and meta-analysis
2023, Behavioural Brain ResearchCitation Excerpt :Interestingly, a recent meta-analysis reported that altered DD is a stable feature of most of psychiatric disorders, with steeper DD in people with major depressive disorder, schizophrenia, borderline personality disorder, bipolar disorder, bulimia nervosa, and binge-eating disorder and shallower DD in people with anorexia nervosa [5]. Steeper DD was also reported in meta-analyses on individuals with narcissistic personality disorders [32], substance related disorders and addictive behaviors [4,70,77,78]), including Internet and gaming addiction ([27,29]), and individuals with ADHD [35,82] relative to healthy controls. Scholars have suggested that abnormal DD in psychopathology may be related to dysfunction of two competing neural systems involved in decision-making: a frontal cortical system involved in executive control and a limbic-subcortical system that drives immediate reward seeking [5].
Theoretical models of types of problematic usage of the Internet: when theorists meet therapists
2022, Current Opinion in Behavioral SciencesCitation Excerpt :In addition, the involvement of attentional bias and implicit expectancies or implicit associations with addiction-related stimuli has been demonstrated for gaming disorder [19,20] and problematic use of social networks [21], use of pornography [22], and buying/shopping [23]. Decision making and other executive functions are also altered in individuals with unspecified PUI [24,25] or specific subtypes of PUI, such as gaming disorder [26] and problematic use of social networks [27]. In summary, the central hypotheses of the most recent theoretical models on PUI are widely supported by current empirical studies.
Problematic usage of the internet and cognition
2022, Current Opinion in Behavioral SciencesCitation Excerpt :The meta-analysis also identified a large effect for delay discounting deficits in PUI, albeit coupled with critical methodological limitations that disallowed robust conclusions to be drawn. A more recent meta-analysis up to 2020 focusing on delay discounting only, replicated the large effects in favor of deficits in delay discounting in including internet gaming disorder (IGD) and PUI, versus controls, and also identified challenging methodological issues around the approaches used to capture those differences [15]. An overview of most recent cognitive findings in PUI and IGD are presented in Table 1.